Create risk time ("Person-Years") in Lexis triangles from population count data.
Data on population size at equidistant dates and age-classes are used to estimate person-time at risk in Lexis-triangles, i.e. classes classified by age, period AND cohort (date of birth). Only works for data where age-classes have the same width as the period-intervals.
N2Y( A, P, N,
data = NULL,
return.dfr = TRUE)A |
Name of the age-variable, which should be numeric, corresponding to the left endpoints of the age intervals. |
P |
Name of the period-variable, which should be numeric, corresponding to the date of population count. |
N |
The population size at date |
data |
A data frame in which arguments are interpreted. |
return.dfr |
Logical. Should the results be returned as a data frame
(default |
The calculation of the risk time from the population figures is done as described in: B. Carstensen: Age-Period-Cohort models for the Lexis diagram. Statistics in Medicine, 26: 3018-3045, 2007.
The number of periods in the result is one less than the number
of dates (nP=length(table(P))) in the input, so the number of
distinct values is 2*(nP-1), because the P in the output
is coded differently for upper and lower Lexis triangles.
The number of age-classes is the same as in the input
(nA=length(table(A))), so the number of distinct values is
2*nA, because the A in the output is coded differently
for upper and lower Lexis triangles.
In the paper "Age-Period-Cohort models for the Lexis diagram" I suggest that the risk time in the lower triangles in the first age-class and in the upper triangles in the last age-class are computed so that the total risk time in the age-class corresponds to the average of the two population figures for the age-class at either end of a period multiplied with the period length. This is the method used.
A data frame with variables A, P and Y,
representing the mean age and period in the Lexis triangles and the
person-time in them, respectively. The person-time is in units of the
distance between population count dates.
If return.dfr=FALSE a three-way table classified by the left end
point of the age-classes and the periods and a factor wh taking
the values up and lo corresponding to upper (early
cohort) and lower (late cohort) Lexis triangles.
Bendix Carstensen, http://bendixcarstensen.com
B. Carstensen: Age-Period-Cohort models for the Lexis diagram. Statistics in Medicine, 26: 3018-3045, 2007.
# Danish population at 1 Jan each year by sex and age data( N.dk ) # An illustrative subset ( Nx <- subset( N.dk, sex==1 & A<5 & P<1975 ) ) # Show the data in tabular form xtabs( N ~ A + P, data=Nx ) # Lexis triangles as data frame Nt <- N2Y( data=Nx, return.dfr=TRUE ) xtabs( Y ~ round(A,2) + round(P,2), data=Nt ) # Lexis triangles as a 3-dim array ftable( N2Y( data=Nx, return.dfr=FALSE ) ) # Calculation of PY for persons born 1970 in 1972 ( N.1.1972 <- subset( Nx, A==1 & P==1972)$N ) ( N.2.1973 <- subset( Nx, A==2 & P==1973)$N ) N.1.1972/3 + N.2.1973/6 N.1.1972/6 + N.2.1973/3 # These numbers can be found in the following plot: # Blue numbers are population size at 1 January # Red numbers are the computed person-years in Lexis triangles: Lexis.diagram(age=c(0,5), date=c(1970,1975), int=1, coh.grid=TRUE ) with( Nx, text(P,A+0.5,paste(N),srt=90,col="blue") ) with( Nt, text(P,A,formatC(Y,format="f",digits=1),col="red") ) text( 1970.5, 2, "Population count 1 January", srt=90, col="blue") text( 1974.5, 2, "Person-\nyears", col="red")
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